基于机器学习从光学相干断层扫描（OCT）图像中检测年龄相关性黄斑变性（AMD）和糖尿病性黄斑水肿（DME）
Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images.
作者信息
Wang Yu, Zhang Yaonan, Yao Zhaomin, Zhao Ruixue, Zhou Fengfeng
机构信息
Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning 110169, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.
Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning 110169, China; College of Electronics and Information Engineering, Xi'an Siyuan University, Xi'an 710038, China;
出版信息
Biomed Opt Express. 2016 Nov 3;7(12):4928-4940. doi: 10.1364/BOE.7.004928. eCollection 2016 Dec 1.
Abstract
Non-lethal macular diseases greatly impact patients' life quality, and will cause vision loss at the late stages. Visual inspection of the optical coherence tomography (OCT) images by the experienced clinicians is the main diagnosis technique. We proposed a computer-aided diagnosis (CAD) model to discriminate age-related macular degeneration (AMD), diabetic macular edema (DME) and healthy macula. The linear configuration pattern (LCP) based features of the OCT images were screened by the Correlation-based Feature Subset (CFS) selection algorithm. And the best model based on the sequential minimal optimization (SMO) algorithm achieved 99.3% in the overall accuracy for the three classes of samples.
摘要
非致死性黄斑疾病对患者的生活质量有很大影响，并将在晚期导致视力丧失。由经验丰富的临床医生对光学相干断层扫描（OCT）图像进行目视检查是主要的诊断技术。我们提出了一种计算机辅助诊断（CAD）模型，用于区分年龄相关性黄斑变性（AMD）、糖尿病性黄斑水肿（DME）和健康黄斑。通过基于相关性的特征子集（CFS）选择算法筛选了OCT图像基于线性配置模式（LCP）的特征。基于序列最小优化（SMO）算法的最佳模型在三类样本的总体准确率上达到了99.3%。
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